
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler,MinMaxScaler,PowerTransformer
from sklearn.model_selection import train_test_split,cross_val_score,KFold,GridSearchCV
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.metrics import r2_score,f1_score,accuracy_score,roc_curve,roc_auc_score,confusion_matrix,classification_report
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from scipy.stats import stats
import scipy.stats as stats
import statsmodels
import statsmodels.api as sm
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
import warnings
warnings.filterwarnings('ignore')
from imblearn.over_sampling import SMOTE
from sklearn.feature_selection import RFE
from mlxtend.feature_selection import SequentialFeatureSelector as sfs


df = pd.read_csv('online_shoppers_intention.csv')
df.head()


print ('Number of rows -',df.shape[0])
print ('Number of columns -',df.shape[1])

Number of rows - 12330
Number of columns - 18


categorical_columns=df.select_dtypes(include='object').columns
numerical_columns=df.select_dtypes(exclude='object').columns


print('categorical columns:',categorical_columns)
print('numerical columns:',numerical_columns)

categorical columns: Index(['Month', 'VisitorType'], dtype='object')
numerical columns: Index(['Administrative', 'Administrative_Duration', 'Informational',
       'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration',
       'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay',
       'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'Weekend',
       'Revenue'],
      dtype='object')


for i in df.select_dtypes(include='object'):
    print('no of categories:',i,'is',len(df[i].value_counts()))

no of categories: Month is 10
no of categories: VisitorType is 3


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12330 entries, 0 to 12329
Data columns (total 18 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Administrative           12330 non-null  int64  
 1   Administrative_Duration  12330 non-null  float64
 2   Informational            12330 non-null  int64  
 3   Informational_Duration   12330 non-null  float64
 4   ProductRelated           12330 non-null  int64  
 5   ProductRelated_Duration  12330 non-null  float64
 6   BounceRates              12330 non-null  float64
 7   ExitRates                12330 non-null  float64
 8   PageValues               12330 non-null  float64
 9   SpecialDay               12330 non-null  float64
 10  Month                    12330 non-null  object 
 11  OperatingSystems         12330 non-null  int64  
 12  Browser                  12330 non-null  int64  
 13  Region                   12330 non-null  int64  
 14  TrafficType              12330 non-null  int64  
 15  VisitorType              12330 non-null  object 
 16  Weekend                  12330 non-null  bool   
 17  Revenue                  12330 non-null  bool   
dtypes: bool(2), float64(7), int64(7), object(2)
memory usage: 1.5+ MB


# Bool - Weekend, Revenue
# Object - Month, VisitorType
# Numerical - Administrative, Administrative_Duration, Informational, Informational_Duration,
# ProductRelated, ProductRelated_Duration, BounceRates, ExitRates, PageValues, SpecialDay, 
# OperatingSystems, Browser, Region, TrafficType


# 5 point summary
df.describe().T


# duration features mean and max value difference shows data is skewed
#product related page is visited by the user more than anyother page
#user visited the website has less influence with respect to special day


df.isnull().sum()

Administrative             0
Administrative_Duration    0
Informational              0
Informational_Duration     0
ProductRelated             0
ProductRelated_Duration    0
BounceRates                0
ExitRates                  0
PageValues                 0
SpecialDay                 0
Month                      0
OperatingSystems           0
Browser                    0
Region                     0
TrafficType                0
VisitorType                0
Weekend                    0
Revenue                    0
dtype: int64


# There are no null values in the datset


df.skew()

Administrative             1.960357
Administrative_Duration    5.615719
Informational              4.036464
Informational_Duration     7.579185
ProductRelated             4.341516
ProductRelated_Duration    7.263228
BounceRates                2.947855
ExitRates                  2.148789
PageValues                 6.382964
SpecialDay                 3.302667
OperatingSystems           2.066285
Browser                    3.242350
Region                     0.983549
TrafficType                1.962987
Weekend                    1.265962
Revenue                    1.909509
dtype: float64


# There are features with high skewness - durations, page_values
# There are features with moderate skewness - bounce_rates, exit_rates


plt.figure(figsize=(100,95))
with plt.style.context({'axes.labelsize':100,
                        'xtick.labelsize':90,
                        'ytick.labelsize':90}):
    ax=sns.heatmap(df.corr()[df.corr()>0.4],annot=True,annot_kws={"size":80},fmt='.2f')
    ax.set_xlabel('x label')


# THERE IS MULTICOLLINEARITY
# PageValue has moderate correlation with revenue column
# This tells the page which is visited higher by the user generating high revenue


sns.pairplot(df,diag_kind='kde',hue='Revenue')

<seaborn.axisgrid.PairGrid at 0x1f13f173520>


# There is overlapping in this data


#numerical features

num=['Administrative_Duration','Informational_Duration','ProductRelated_Duration','BounceRates','ExitRates','PageValues']
df.hist(column=num)
plt.tight_layout()


#we can see the right skewed distribution
#there are outliers in exit rates and bounce rates,shows there are some unusual or rare events happened


col=['Administrative','Informational','ProductRelated','SpecialDay','OperatingSystems','Browser','Region','TrafficType','Month','VisitorType','Weekend']
c=1
plt.figure(figsize=(15,10))
for i in col:
    ax=plt.subplot(6,2,c)
    sns.countplot(x=df[i],ax=ax)
    c=c+1
    plt.tight_layout()


#repeated visit is for product related page
#operating system 2 has high usage
#from region 1 high number of visitor
#march,may,nov,dec shows high visitor than others show some seasonility in visiting
#weekday has high number of visitors foronline shopping than weekend
#there are more number of returning visitor than new visitor. this implies we should do more marketing to attract new visitors


#how features are having relation with target i.e revenue


num=['Administrative_Duration','Informational_Duration','ProductRelated_Duration','BounceRates','ExitRates','PageValues']
c=0
plt.figure(figsize=(30,45))
with plt.style.context({'axes.labelsize':24,
                        'xtick.labelsize':24,
                        'ytick.labelsize':24}):
    for i in num:
        c=c+1
        ax=plt.subplot(6,2,c)
        sns.boxplot(y=df[i],x=df['Revenue'],ax=ax)
        ax.set_title('Revenue generated based on '+i)
        plt.tight_layout()


#revenue generated by administrative page is slightly high than revenue not genrated by that page,shows the page is somehow 
#profitable,this is same with respect to information related page
#with respect to product related the revenue is neither profit nor loss
#revenue generated by high page value  is slightly higher than revenue genrated by the low page value


col=['Administrative','Informational','ProductRelated','SpecialDay','OperatingSystems','Browser','Region','TrafficType','Month','VisitorType','Weekend']
c=0
plt.figure(figsize=(30,45))
with plt.style.context({'axes.labelsize':24,
                        'xtick.labelsize':24,
                        'ytick.labelsize':24}):

    for i in col:
        c=c+1
        ax=plt.subplot(6,2,c)
        sns.countplot(x=df[i],hue=df['Revenue'],ax=ax)
        plt.tight_layout()


for i in num:
    m=np.mean(df[i])
    s=np.std(df[i])
    thresh=3
    z=(df[i]-m)/s
    outlier=[x for x in np.abs(z) if x>thresh]
    print(i)
    print('outlier count',len(outlier))
    print('percentage of outliers',len(outlier)/df[i].shape[0]*100)
    print()

Administrative_Duration
outlier count 232
percentage of outliers 1.8815896188158963

Informational_Duration
outlier count 230
percentage of outliers 1.8653690186536902

ProductRelated_Duration
outlier count 219
percentage of outliers 1.7761557177615572

BounceRates
outlier count 708
percentage of outliers 5.742092457420925

ExitRates
outlier count 713
percentage of outliers 5.78264395782644

PageValues
outlier count 259
percentage of outliers 2.1005677210056772


df.head()


df1=df.copy()
df1.head()


#labelencoding
from sklearn.preprocessing import LabelEncoder
LE=LabelEncoder()
df1['Weekend']=LE.fit_transform(df1['Weekend'])
df1['Revenue']=LE.fit_transform(df1['Revenue'])


df1.head()


df1.dtypes

Administrative               int64
Administrative_Duration    float64
Informational                int64
Informational_Duration     float64
ProductRelated               int64
ProductRelated_Duration    float64
BounceRates                float64
ExitRates                  float64
PageValues                 float64
SpecialDay                 float64
Month                       object
OperatingSystems             int64
Browser                      int64
Region                       int64
TrafficType                  int64
VisitorType                 object
Weekend                      int64
Revenue                      int64
dtype: object


for i in ['Month','OperatingSystems','Browser','Region','TrafficType','VisitorType']:
    df1[i]=df1[i].astype('object')


cat=['Month','OperatingSystems','Browser','Region','TrafficType','VisitorType']
dummy=pd.get_dummies(df1[cat],drop_first=True)


df1=df1.drop(columns=['Month','OperatingSystems','Browser','Region','TrafficType','VisitorType'])
df1.head()


df_Prepared=pd.concat([df1,dummy],axis=1)
df_Prepared.head()


sns.heatmap(df_Prepared[num].corr(),annot=True)

<AxesSubplot:>


#There is no Multicollinearity


sns.countplot(df_Prepared['Revenue'])

<AxesSubplot:xlabel='Revenue', ylabel='count'>


# yes there is class imbalance in dataset we can do oversampling and undersampling techniques like smote,Nearmiss


# we can treat class imbalance using smote after doing train test split
# treating class imbalance in ytrain because while model building
#the model should not be biased to one particlur class because it has high records
# because it tends to affect accuracy of the prediction


shapiro=pd.DataFrame(index=df.select_dtypes(include='number').columns,columns=['Shapiro p value'])
for i in df.select_dtypes(include='number').columns:
    Tstat,pvalue=stats.shapiro(df[i])
    shapiro.loc[i,'Shapiro p value']=pvalue
shapiro


#Data is not normal


#so We go with non parametric test 
from sklearn.preprocessing import LabelEncoder
LE=LabelEncoder()
df['Weekend']=LE.fit_transform(df['Weekend'])
df['Revenue']=LE.fit_transform(df['Revenue'])


Mwu=pd.DataFrame(index=df.select_dtypes(include='number').drop(df['Revenue']).columns,columns=['pval'])
for i in df.select_dtypes(include='number').drop(df['Revenue']).columns:
    group_1=df[i][df.Revenue==0]
    group_2=df[i][df.Revenue==1]
    hstat,pval=stats.mannwhitneyu(group_1,group_2)
    Mwu.loc[i,'pval']=pval
Mwu


#Region is statistically significant in predicting the target variable


chisquare=pd.DataFrame(index=df.select_dtypes(include='object').columns,columns=['pval'])
for i in df.select_dtypes(include='object').columns:
    group_1=df[i][df.Revenue==0]
    group_2=df[i][df.Revenue==1]
    chi,pval,sdf,sf=stats.chi2_contingency(pd.crosstab(index=df[i],columns=df['Revenue']))
    chisquare.loc[i,'pval']=pval
chisquare


# if palue<0.05 those feature are statsically sigifcant for predicting target class
# tht features are associated with target colum.
#Though some of the columns are statistical insiginicant we will also use those columns for model prediction as their may some
#sensitive data's


x = df_Prepared.drop(columns='Revenue')
y = df_Prepared['Revenue']


xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=10)


mu = x.mean()
tstat, pval = stats.ttest_1samp(xtrain.select_dtypes(exclude = 'object'),popmean = mu)
print(pval)

[0.93509308 0.72917488 0.75990768 0.75366495 0.36404401 0.47110559
 0.90074881 0.88713106 0.64765601 0.41075795 0.79142293 0.43600027
 0.9155511  0.61943922 0.32111138 0.45512202 0.90007559 0.37715726
 0.32837386 0.972444   0.36098451 0.43058388 0.79626568 0.92033285
 0.36909608 0.65329631 0.45226993 0.64732773 0.50314526 0.92022868
 0.50259395 0.72468634 0.77648644 0.34870198 0.76418438 0.91704688
 0.92033285 0.13358344 0.34773912 0.71682786 0.4260716  0.38621059
 0.71704558 0.56074211 0.58624303 0.38587667 0.98708001 0.74821969
 0.67434538 0.80886463 0.76686354 0.77996066 0.58938001 0.9014143
 0.37821994 0.6500734  0.4829283  0.76418438 0.76321397 0.69723651
 0.79102351 0.60330312 0.76418438 1.         0.97695624 0.97271677
 0.84335225 0.75155491]


# All the pval > 0.05. Hence train set is a representative of Original Data


mu = x.mean()
tstat, pval = stats.ttest_1samp(xtest.select_dtypes(exclude = 'object'),popmean = mu)
print(pval)

[0.90013339 0.57080859 0.61983673 0.62822478 0.11722141 0.18569464
 0.84890227 0.82791604 0.51799135 0.18685957 0.68446715 0.24747657
 0.8693605  0.42676894 0.08668858 0.26591331 0.84756588 0.16906694
 0.1654201  0.95805218 0.16388188 0.23802921 0.69961904 0.88752968
 0.0089159  0.27140372 0.32325032 0.48688353 0.36360574 0.87923104
 0.27651118 0.60895088 0.63676763 0.08675704 0.         0.87556511
 0.88752968 0.22043719 0.24322794 0.57249629 0.23416428 0.20414865
 0.5645247  0.35615514 0.38854935 0.15001619 0.98024141 0.62236978
 0.51530229 0.71524926 0.63863336 0.6771496  0.31685198 0.84801412
 0.28492644 0.47015752 0.32325822 0.         0.65189914 0.62260632
 0.65760463 0.         0.         1.         0.96431253 0.95814204
 0.77206557 0.6326051 ]


# All the pval > 0.05. Hence Test set is a representative of Original Data


print(xtrain.shape)
print(ytrain.shape)
print(xtest.shape)
print(ytest.shape)

(8631, 68)
(8631,)
(3699, 68)
(3699,)


from sklearn.preprocessing import PowerTransformer
pt = PowerTransformer()
x_scaled = pd.DataFrame(pt.fit_transform(xtrain),columns=xtrain.columns)


xtest_scaled=pd.DataFrame(pt.transform(xtest),columns=xtest.columns)


x_scaled.skew()

Administrative                    0.247594
Administrative_Duration           0.150628
Informational                     1.419061
Informational_Duration            1.559372
ProductRelated                   -0.001746
                                   ...    
TrafficType_18                   35.077464
TrafficType_19                   26.767526
TrafficType_20                    7.689632
VisitorType_Other                12.077575
VisitorType_Returning_Visitor    -2.038895
Length: 68, dtype: float64


xtest_scaled.skew()

Administrative                    0.223879
Administrative_Duration           0.123979
Informational                     1.370144
Informational_Duration            1.518069
ProductRelated                   -0.066765
                                   ...    
TrafficType_18                   35.085597
TrafficType_19                   27.155098
TrafficType_20                    7.730432
VisitorType_Other                11.580851
VisitorType_Returning_Visitor    -1.993811
Length: 68, dtype: float64


# The skewness is treated by Power Transformer


from sklearn.model_selection import cross_val_score, KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.metrics import classification_report, accuracy_score, roc_auc_score


# Logistic Regression


x=sm.add_constant(xtrain)
y=ytrain
log1=sm.Logit(y,x).fit(method='cg')
print(log1.summary())

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.595673
         Iterations: 35
         Function evaluations: 79
         Gradient evaluations: 71
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                Revenue   No. Observations:                 8631
Model:                          Logit   Df Residuals:                     8563
Method:                           MLE   Df Model:                           67
Date:                Mon, 18 Oct 2021   Pseudo R-squ.:                 -0.3899
Time:                        15:19:25   Log-Likelihood:                -5141.3
converged:                      False   LL-Null:                       -3698.9
Covariance Type:            nonrobust   LLR p-value:                     1.000
=================================================================================================
                                    coef    std err          z      P>|z|      [0.025      0.975]
-------------------------------------------------------------------------------------------------
const                            -0.0110      0.189     -0.058      0.953      -0.381       0.359
Administrative                   -0.0056      0.014     -0.406      0.685      -0.033       0.021
Administrative_Duration          -0.0026      0.000     -8.019      0.000      -0.003      -0.002
Informational                    -0.0006      0.032     -0.018      0.986      -0.063       0.061
Informational_Duration            0.0001      0.000      0.503      0.615      -0.000       0.001
ProductRelated                   -0.0417      0.002    -18.914      0.000      -0.046      -0.037
ProductRelated_Duration           0.0004    3.7e-05      9.802      0.000       0.000       0.000
BounceRates                      -0.0004      1.189     -0.000      1.000      -2.332       2.331
ExitRates                        -0.0008      1.312     -0.001      1.000      -2.572       2.570
PageValues                        0.0885      0.004     24.426      0.000       0.081       0.096
SpecialDay                       -0.0010      0.141     -0.007      0.994      -0.278       0.276
Weekend                          -0.0024      0.061     -0.038      0.969      -0.122       0.118
Month_Dec                        -0.0017      0.159     -0.011      0.991      -0.314       0.310
Month_Feb                        -0.0003      0.235     -0.001      0.999      -0.460       0.459
Month_Jul                        -0.0004      0.195     -0.002      0.998      -0.382       0.381
Month_June                       -0.0003      0.211     -0.002      0.999      -0.414       0.414
Month_Mar                        -0.0023      0.156     -0.015      0.988      -0.309       0.304
Month_May                        -0.0038      0.152     -0.025      0.980      -0.302       0.295
Month_Nov                        -0.0012      0.153     -0.008      0.994      -0.302       0.299
Month_Oct                        -0.0003      0.192     -0.002      0.999      -0.377       0.377
Month_Sep                        -0.0004      0.195     -0.002      0.999      -0.383       0.382
OperatingSystems_2               -0.0052      0.143     -0.037      0.971      -0.285       0.274
OperatingSystems_3               -0.0027      0.149     -0.018      0.986      -0.295       0.290
OperatingSystems_4               -0.0004      0.148     -0.002      0.998      -0.291       0.290
OperatingSystems_5            -3.642e-06   7.46e+06  -4.88e-13      1.000   -1.46e+07    1.46e+07
OperatingSystems_6            -2.614e-05      0.570  -4.59e-05      1.000      -1.117       1.117
OperatingSystems_7            -1.422e-05      0.853  -1.67e-05      1.000      -1.672       1.672
OperatingSystems_8            -6.723e-05      0.505     -0.000      1.000      -0.991       0.990
Browser_2                        -0.0069      0.143     -0.048      0.962      -0.286       0.273
Browser_3                        -0.0001      0.307     -0.000      1.000      -0.601       0.601
Browser_4                        -0.0006      0.179     -0.003      0.997      -0.351       0.349
Browser_5                        -0.0004      0.190     -0.002      0.998      -0.373       0.372
Browser_6                        -0.0002      0.255     -0.001      0.999      -0.500       0.500
Browser_7                     -4.713e-05      0.396     -0.000      1.000      -0.776       0.776
Browser_8                        -0.0002      0.233     -0.001      0.999      -0.458       0.457
Browser_9                     -1.221e-06      2.304   -5.3e-07      1.000      -4.517       4.517
Browser_10                       -0.0001      0.261     -0.000      1.000      -0.513       0.512
Browser_11                    -3.642e-06   7.46e+06  -4.88e-13      1.000   -1.46e+07    1.46e+07
Browser_12                     1.684e-06      1.117   1.51e-06      1.000      -2.189       2.189
Browser_13                     -5.13e-05      0.708  -7.25e-05      1.000      -1.388       1.387
Region_2                         -0.0010      0.092     -0.010      0.992      -0.182       0.180
Region_3                         -0.0022      0.070     -0.031      0.976      -0.140       0.136
Region_4                         -0.0010      0.092     -0.011      0.991      -0.182       0.180
Region_5                         -0.0003      0.161     -0.002      0.999      -0.315       0.315
Region_6                         -0.0008      0.104     -0.008      0.994      -0.204       0.202
Region_7                         -0.0007      0.108     -0.007      0.995      -0.213       0.212
Region_8                         -0.0005      0.134     -0.003      0.997      -0.264       0.263
Region_9                         -0.0004      0.136     -0.003      0.997      -0.266       0.265
TrafficType_2                    -0.0023      0.078     -0.029      0.977      -0.156       0.151
TrafficType_3                    -0.0026      0.083     -0.031      0.975      -0.166       0.161
TrafficType_4                    -0.0010      0.110     -0.009      0.993      -0.217       0.215
TrafficType_5                    -0.0002      0.188     -0.001      0.999      -0.368       0.368
TrafficType_6                    -0.0004      0.145     -0.003      0.998      -0.284       0.283
TrafficType_7                 -5.695e-06      0.461  -1.24e-05      1.000      -0.904       0.904
TrafficType_8                    -0.0001      0.168     -0.001      0.999      -0.328       0.328
TrafficType_9                 -4.551e-05      0.438     -0.000      1.000      -0.858       0.858
TrafficType_10                   -0.0003      0.145     -0.002      0.998      -0.284       0.283
TrafficType_11                   -0.0002      0.194     -0.001      0.999      -0.381       0.381
TrafficType_12                 -2.72e-06      2.011  -1.35e-06      1.000      -3.941       3.941
TrafficType_13                   -0.0009      0.120     -0.008      0.994      -0.236       0.234
TrafficType_14                -3.397e-06      0.814  -4.17e-06      1.000      -1.596       1.596
TrafficType_15                -6.628e-05      0.417     -0.000      1.000      -0.816       0.816
TrafficType_16                -1.449e-07      1.388  -1.04e-07      1.000      -2.721       2.721
TrafficType_17                -2.701e-06      2.014  -1.34e-06      1.000      -3.946       3.946
TrafficType_18                -1.531e-05      0.820  -1.87e-05      1.000      -1.607       1.607
TrafficType_19                -2.042e-05      0.678  -3.01e-05      1.000      -1.328       1.328
TrafficType_20                   -0.0002      0.234     -0.001      0.999      -0.459       0.459
VisitorType_Other                -0.0001      0.417     -0.000      1.000      -0.817       0.817
VisitorType_Returning_Visitor    -0.0099      0.083     -0.119      0.905      -0.173       0.153
=================================================================================================


lr = LogisticRegression()
lr.fit(x_scaled, ytrain)
pred_lr = lr.predict(xtest_scaled)
print (classification_report(ytest, pred_lr))

              precision    recall  f1-score   support

           0       0.93      0.95      0.94      3115
           1       0.71      0.61      0.65       584

    accuracy                           0.90      3699
   macro avg       0.82      0.78      0.80      3699
weighted avg       0.89      0.90      0.90      3699


kf = KFold (n_splits =5,shuffle = True,random_state = 10)
score_lr = cross_val_score (lr, x_scaled, ytrain,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_lr))
print ('Variance error:',np.std(score_lr,ddof=1))

Bias score: 0.9117088872368054
Variance error: 0.00263945818000346


# Naive_bayes


gnb = GaussianNB()
gnb.fit (x_scaled, ytrain)
pred_gnb = gnb.predict(xtest_scaled)
print (classification_report (ytest, pred_gnb))

              precision    recall  f1-score   support

           0       0.96      0.04      0.08      3115
           1       0.16      0.99      0.28       584

    accuracy                           0.19      3699
   macro avg       0.56      0.52      0.18      3699
weighted avg       0.83      0.19      0.11      3699


kf = KFold (n_splits =5, shuffle = True, random_state = 10)
score_gnb = cross_val_score (gnb, x_scaled, ytrain,cv=kf,scoring = 'roc_auc')
print ('Bias score:',np.mean(score_gnb))
print('Variance error:',np.std(score_gnb,ddof=1))

Bias score: 0.5467743430553775
Variance error: 0.04075739621955529


# K Neighbors Classifier


knn = KNeighborsClassifier()
knn.fit (x_scaled, ytrain)
pred_knn = knn.predict(xtest_scaled)
print (classification_report (ytest, pred_knn))

              precision    recall  f1-score   support

           0       0.88      0.97      0.92      3115
           1       0.61      0.27      0.37       584

    accuracy                           0.86      3699
   macro avg       0.74      0.62      0.65      3699
weighted avg       0.83      0.86      0.83      3699


kf = KFold (n_splits =5, shuffle = True, random_state = 10)
score_knn = cross_val_score (knn, x_scaled, ytrain,cv=kf,scoring = 'roc_auc')
print ('Bias score:',np.mean(score_knn))
print('Variance error:',np.std(score_knn,ddof=1))

Bias score: 0.7863411273856273
Variance error: 0.014689176509020463


# Decision Tree Classifier


dt = DecisionTreeClassifier(random_state=10)
dt.fit(x_scaled, ytrain)
pred_dt = dt.predict(xtest_scaled)
print (classification_report (ytest, pred_dt))

              precision    recall  f1-score   support

           0       0.92      0.92      0.92      3115
           1       0.56      0.57      0.57       584

    accuracy                           0.86      3699
   macro avg       0.74      0.74      0.74      3699
weighted avg       0.86      0.86      0.86      3699


kf = KFold (n_splits =5, shuffle = True, random_state = 10)
score_dt = cross_val_score (dt, x_scaled, ytrain,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_dt))
print('Variance error:',np.std(score_dt,ddof=1))

Bias score: 0.7327562971929138
Variance error: 0.014080161633066409


# Random Forest Classifier


rf = RandomForestClassifier()
rf.fit(x_scaled, ytrain)
pred_rf = rf.predict(xtest_scaled)
print (classification_report (ytest, pred_rf))

              precision    recall  f1-score   support

           0       0.91      0.97      0.94      3115
           1       0.79      0.51      0.62       584

    accuracy                           0.90      3699
   macro avg       0.85      0.74      0.78      3699
weighted avg       0.89      0.90      0.89      3699


kf = KFold (n_splits =5, shuffle = True, random_state = 10)
score_rf = cross_val_score(rf, x_scaled, ytrain,cv=kf,scoring='roc_auc')
print ('Bias score:',np.mean(score_rf))
print('Variance error:',np.std(score_rf,ddof=1))

Bias score: 0.9245884870807393
Variance error: 0.0058387480743214894


models=[]
models.append(('Logistic',lr))
models.append(('NaiveBayes',gnb))
models.append(('KNN',knn))
models.append(('DecisionTree',dt))
models.append(('RandomForest',rf))


data=[]
data1=[]
results=[]
names=[]
for name , model in models:
    kfold =KFold (n_splits =5, shuffle = True, random_state = 10)
    cv_results=cross_val_score(model,x_scaled,ytrain,scoring='roc_auc')
    results.append(cv_results)
    names.append(name)
    data.append(np.mean(cv_results))
    data1.append(np.std(cv_results,ddof=1))
    a = pd.DataFrame({'Bias score':data,'Variance error':data1},index=names)
a.head()


# BY THE BASE MODEL EVALUATION RANDOM FOREST CLASSIFIER IS PERFORMING BETTER THAN OTHER MODELS


sms=SMOTE(random_state=10,sampling_strategy=0.7)
xtrain_sm,ytrain_sm=sms.fit_resample(x_scaled,ytrain)
print(xtrain_sm.shape)
print(ytrain_sm.shape)

(12421, 68)
(12421,)


xtrain.shape

(8631, 68)


ytrain.shape

(8631,)


sns.countplot(ytrain_sm)

<AxesSubplot:xlabel='Revenue', ylabel='count'>


#logistic Regression
lrsm = LogisticRegression()
lrsm.fit(xtrain_sm, ytrain_sm)
pred_lrsm = lrsm.predict(xtest_scaled)
print (classification_report(ytest, pred_lrsm))

              precision    recall  f1-score   support

           0       0.96      0.89      0.92      3115
           1       0.58      0.79      0.67       584

    accuracy                           0.88      3699
   macro avg       0.77      0.84      0.80      3699
weighted avg       0.90      0.88      0.88      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_lrsm = cross_val_score (lrsm, xtrain_sm, ytrain_sm,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_lrsm))
print ('Variance score:',np.std(score_lrsm,ddof=1))

Bias score: 0.9335101941587208
Variance score: 0.004279587267516743


#Naives Bayes
gnbsm = GaussianNB()
gnbsm.fit (xtrain_sm,ytrain_sm)
pred_gnbsm = gnbsm.predict(xtest_scaled)
print (classification_report (ytest, pred_gnbsm))

              precision    recall  f1-score   support

           0       0.96      0.04      0.08      3115
           1       0.16      0.99      0.28       584

    accuracy                           0.19      3699
   macro avg       0.56      0.52      0.18      3699
weighted avg       0.84      0.19      0.11      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_gnbsm = cross_val_score (gnbsm, xtrain_sm, ytrain_sm,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_gnbsm))
print('Variance:',np.std(score_gnbsm,ddof=1))

Bias score: 0.5973383733592823
Variance: 0.05822306927651666


#KNN
knnsm = KNeighborsClassifier()
knnsm.fit (xtrain_sm, ytrain_sm)
pred_knnsm = knnsm.predict(xtest_scaled)
print (classification_report (ytest, pred_knnsm))

              precision    recall  f1-score   support

           0       0.93      0.81      0.87      3115
           1       0.41      0.68      0.51       584

    accuracy                           0.79      3699
   macro avg       0.67      0.75      0.69      3699
weighted avg       0.85      0.79      0.81      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_knnsm = cross_val_score (knnsm, xtrain_sm, ytrain_sm,cv=kf,scoring = 'roc_auc')
print ('Bias score:',np.mean(score_knnsm))
print('Variance:',np.std(score_knnsm,ddof=1))

Bias score: 0.9429169928693
Variance: 0.005659173747841147


#DECISION TREE
dtsm = DecisionTreeClassifier(random_state=10)
dtsm.fit(xtrain_sm, ytrain_sm)
pred_dtsm = dtsm.predict(xtest_scaled)
print (classification_report (ytest, pred_dtsm))

              precision    recall  f1-score   support

           0       0.92      0.90      0.91      3115
           1       0.53      0.59      0.56       584

    accuracy                           0.85      3699
   macro avg       0.72      0.74      0.73      3699
weighted avg       0.86      0.85      0.85      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_dtsm = cross_val_score (dtsm, xtrain_sm, ytrain_sm,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_dtsm))
print('Variance:',np.std(score_dtsm,ddof=1))

Bias score: 0.8836800429341178
Variance: 0.006156145681644643


#RandomForestClassifier
rfsm = RandomForestClassifier()
rfsm.fit(xtrain_sm, ytrain_sm)
pred_rfsm = rfsm.predict(xtest_scaled)
print (classification_report (ytest, pred_rfsm))

              precision    recall  f1-score   support

           0       0.94      0.94      0.94      3115
           1       0.67      0.66      0.67       584

    accuracy                           0.90      3699
   macro avg       0.80      0.80      0.80      3699
weighted avg       0.90      0.90      0.90      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_rfsm = cross_val_score(rfsm, xtrain_sm, ytrain_sm,cv=kf, scoring='roc_auc')
print ('Bias score:',np.mean(score_rfsm))
print('Variance:',np.std(score_rfsm,ddof=1))

Bias score: 0.9823153505549641
Variance: 0.0015935888226316253


#AFTER CLASS IMBALANCE THE BASE MODEL IS PERFORMING WELL


modelssm=[]
modelssm.append(('Logistic1',lrsm))
modelssm.append(('NaiveBayes1',gnbsm))
modelssm.append(('KNN1',knnsm))
modelssm.append(('DecisionTree1',dtsm))
modelssm.append(('RandomForest1',rfsm))


datasm=[]
data1sm=[]
resultssm=[]
namessm=[]
for namesm , model in modelssm:
    kf =KFold (n_splits =5, shuffle = True, random_state = 10)
    cv_resultssm=cross_val_score(model,xtrain_sm,ytrain_sm,cv=kf,scoring='roc_auc')
    resultssm.append(cv_resultssm)
    namessm.append(namesm)
    datasm.append(np.mean(cv_resultssm))
    data1sm.append(np.std(cv_resultssm,ddof=1))
    a1 = pd.DataFrame({'Bias score':datasm,'Variance error':data1sm},index=namessm)
a1.head()


#Decision Tree Tuning
params={'criterion':['entropy','gini'],'min_samples_split':range(2,10),'max_depth':range(2,10)}
kf=KFold(5,random_state=10,shuffle=True)
gs=GridSearchCV(dtsm,cv=kf,param_grid=params,scoring='roc_auc')
gs.fit(xtrain_sm,ytrain_sm)
gs.best_params_

{'criterion': 'entropy', 'max_depth': 7, 'min_samples_split': 8}


dtsm_Tuned = DecisionTreeClassifier(criterion='entropy', max_depth= 7, min_samples_split=8)
dtsm_Tuned.fit(xtrain_sm, ytrain_sm)
pred_dtsm_tuned = dtsm_Tuned.predict(xtest_scaled)
print (classification_report (ytest, pred_dtsm_tuned))

              precision    recall  f1-score   support

           0       0.95      0.90      0.92      3115
           1       0.59      0.75      0.66       584

    accuracy                           0.88      3699
   macro avg       0.77      0.83      0.79      3699
weighted avg       0.89      0.88      0.88      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_dtsm_Tuned = cross_val_score (dtsm_Tuned, xtrain_sm, ytrain_sm,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_dtsm_Tuned))
print('Variance:',np.std(score_dtsm_Tuned,ddof=1))

Bias score: 0.9538959655410479
Variance: 0.0021851944518763576


params = {'n_neighbors':np.arange(1,25,2),'metric':['hamming','euclidean','manhattan','Chebyshev']}
kf = KFold(n_splits=5,shuffle=True,random_state=10)
GS = GridSearchCV(estimator=knn,param_grid=params,cv=kf,scoring='roc_auc')
GS.fit(xtrain_sm,ytrain_sm)
GS.best_params_

{'metric': 'manhattan', 'n_neighbors': 5}


#KNNTuned
knnsm_Tuned = KNeighborsClassifier(n_neighbors=5,metric='manhattan')
knnsm_Tuned.fit (xtrain_sm, ytrain_sm)
pred_knnsm_Tuned = knnsm.predict(xtest_scaled)
print (classification_report (ytest, pred_knnsm_Tuned))

              precision    recall  f1-score   support

           0       0.93      0.81      0.87      3115
           1       0.41      0.68      0.51       584

    accuracy                           0.79      3699
   macro avg       0.67      0.75      0.69      3699
weighted avg       0.85      0.79      0.81      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_knnsm_Tuned = cross_val_score (knnsm_Tuned, xtrain_sm, ytrain_sm,cv=kf,scoring = 'roc_auc')
print ('Bias score:',np.mean(score_knnsm_Tuned))
print('Variance:',np.std(score_knnsm_Tuned,ddof=1))

Bias score: 0.9555412217657102
Variance: 0.0030833965844697586


params=[{'criterion' : ['entropy', 'gini'],
'n_estimators':[90,100,150,200],
'max_depth' :[10,15,20],
'min_samples_split' : [2,5,8]}]
kf = KFold(n_splits=5,shuffle=True,random_state=10)
GS = GridSearchCV(estimator=rfsm,param_grid=params,cv=kf,scoring='roc_auc')
GS.fit(xtrain_sm,ytrain_sm)
GS.best_params_

{'criterion': 'entropy',
 'max_depth': 20,
 'min_samples_split': 2,
 'n_estimators': 200}


#RandomForestClassifier
rfsm_Tuned = RandomForestClassifier(criterion='entropy',max_depth=20,min_samples_split=2,n_estimators=200)
rfsm_Tuned.fit(xtrain_sm, ytrain_sm)
pred_rfsm_Tuned = rfsm_Tuned.predict(xtest_scaled)
print (classification_report (ytest, pred_rfsm_Tuned))

              precision    recall  f1-score   support

           0       0.94      0.94      0.94      3115
           1       0.67      0.69      0.68       584

    accuracy                           0.90      3699
   macro avg       0.81      0.81      0.81      3699
weighted avg       0.90      0.90      0.90      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_rfsm_Tuned = cross_val_score(rfsm_Tuned, xtrain_sm, ytrain_sm,cv=kf, scoring='roc_auc')
print ('Bias score:',np.mean(score_rfsm_Tuned))
print('Variance:',np.std(score_rfsm_Tuned,ddof=1))

Bias score: 0.9821150660138269
Variance: 0.001518579355933902


#TO TUNE THE LOGISTIC REGRESSION WE HAVE TO DO FEATURE SELECTION.


rfe=RFE(estimator=lr,n_features_to_select=5)
rfe.fit(xtrain_sm,ytrain_sm)

RFE(estimator=LogisticRegression(), n_features_to_select=5)


feat= pd.Series(data= rfe.ranking_, index= xtrain_sm.columns)
sig= feat[feat==1].index
sig

Index(['BounceRates', 'PageValues', 'Month_Nov', 'TrafficType_15',
       'VisitorType_Returning_Visitor'],
      dtype='object')


## forward selection


kf=KFold(n_splits=5,shuffle=True,random_state=10)
fl=sfs(estimator=lr,k_features='best',scoring='roc_auc',cv=kf,forward=False)
fl.fit(xtrain_sm,ytrain_sm)
be=list(fl.k_feature_names_)
be

['Administrative',
 'Informational',
 'Informational_Duration',
 'ProductRelated',
 'ProductRelated_Duration',
 'ExitRates',
 'PageValues',
 'Month_Dec',
 'Month_Feb',
 'Month_Jul',
 'Month_Mar',
 'Month_May',
 'Month_Nov',
 'OperatingSystems_2',
 'OperatingSystems_6',
 'OperatingSystems_7',
 'OperatingSystems_8',
 'Browser_3',
 'Browser_6',
 'Browser_10',
 'Browser_12',
 'Region_2',
 'Region_3',
 'Region_4',
 'Region_5',
 'Region_7',
 'Region_9',
 'TrafficType_2',
 'TrafficType_3',
 'TrafficType_4',
 'TrafficType_5',
 'TrafficType_8',
 'TrafficType_10',
 'TrafficType_11',
 'TrafficType_13',
 'TrafficType_15',
 'TrafficType_16',
 'TrafficType_20',
 'VisitorType_Other',
 'VisitorType_Returning_Visitor']


# backward selection


f_ele=sfs(estimator=lr,k_features='best',forward=True,cv=kf,scoring='roc_auc')
f_ele.fit(xtrain_sm,ytrain_sm)
fe1=list(f_ele.k_feature_names_)
fe1

['Administrative',
 'ProductRelated',
 'ProductRelated_Duration',
 'ExitRates',
 'PageValues',
 'Month_Dec',
 'Month_Feb',
 'Month_Jul',
 'Month_Mar',
 'Month_May',
 'Month_Nov',
 'Month_Sep',
 'OperatingSystems_2',
 'OperatingSystems_4',
 'OperatingSystems_6',
 'OperatingSystems_7',
 'OperatingSystems_8',
 'Browser_2',
 'Browser_3',
 'Browser_4',
 'Browser_6',
 'Browser_9',
 'Browser_10',
 'Browser_12',
 'Region_2',
 'Region_3',
 'Region_4',
 'Region_5',
 'Region_7',
 'Region_9',
 'TrafficType_2',
 'TrafficType_3',
 'TrafficType_4',
 'TrafficType_5',
 'TrafficType_6',
 'TrafficType_7',
 'TrafficType_8',
 'TrafficType_10',
 'TrafficType_11',
 'TrafficType_12',
 'TrafficType_13',
 'TrafficType_15',
 'TrafficType_16',
 'TrafficType_17',
 'TrafficType_18',
 'TrafficType_20',
 'VisitorType_Other',
 'VisitorType_Returning_Visitor']


#Building Tuned Logistic Regression Model


def perf2(model,belist):
    model.fit(xtrain_sm[belist],ytrain_sm)
    ypred_test_Tuned=model.predict(xtest_scaled[belist])
    kf=KFold(n_splits=5,shuffle=True,random_state=10)
    scores=cross_val_score(model,xtrain_sm[belist],ytrain_sm,cv=kf,scoring='roc_auc')
    print(scores,'\n')
    print('average scores',np.mean(scores))
    print('bias score',np.mean(scores))
    print('variance score',np.std(scores,ddof=1))
    print('\nclassification report for test data')
    print(classification_report(ytest,ypred_test_Tuned))


#Recursive Feature Elimination
lr=LogisticRegression()
perf2(lr,sig)

[0.92585213 0.93262592 0.92490698 0.92134672 0.93277162] 

average scores 0.9275006750632185
bias score 0.9275006750632185
variance score 0.005034063792660561

classification report for test data
              precision    recall  f1-score   support

           0       0.96      0.89      0.92      3115
           1       0.57      0.78      0.66       584

    accuracy                           0.87      3699
   macro avg       0.76      0.83      0.79      3699
weighted avg       0.90      0.87      0.88      3699


#FORWARD SELECTION
perf2(lr,be)

[0.93223434 0.93766826 0.93355854 0.929515   0.93915462] 

average scores 0.9344261517510496
bias score 0.9344261517510496
variance score 0.003954295365617345

classification report for test data
              precision    recall  f1-score   support

           0       0.96      0.89      0.92      3115
           1       0.57      0.78      0.66       584

    accuracy                           0.87      3699
   macro avg       0.76      0.84      0.79      3699
weighted avg       0.90      0.87      0.88      3699


#BACKWARD SELECTION
perf2(lr,fe1)

[0.93175282 0.93749389 0.93353788 0.92930986 0.93942831] 

average scores 0.9343045529486173
bias score 0.9343045529486173
variance score 0.004137355656329273

classification report for test data
              precision    recall  f1-score   support

           0       0.96      0.89      0.92      3115
           1       0.57      0.78      0.66       584

    accuracy                           0.87      3699
   macro avg       0.76      0.84      0.79      3699
weighted avg       0.90      0.87      0.88      3699


from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier


AB=AdaBoostClassifier(random_state=10)
params={'n_estimators':np.arange(1,100)}
kf=KFold(n_splits=5,shuffle=True,random_state=10)
gs=GridSearchCV(AB,params,cv=kf,scoring='roc_auc')
gs.fit(xtrain_sm,ytrain_sm)
gs.best_params_

{'n_estimators': 99}


AB=AdaBoostClassifier(n_estimators=99,random_state=10)
AB.fit(xtrain_sm, ytrain_sm)
pred_AB = AB.predict(xtest_scaled)
print (classification_report (ytest, pred_AB))

              precision    recall  f1-score   support

           0       0.94      0.92      0.93      3115
           1       0.63      0.69      0.66       584

    accuracy                           0.89      3699
   macro avg       0.79      0.81      0.80      3699
weighted avg       0.89      0.89      0.89      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_AB = cross_val_score(AB, xtrain_sm, ytrain_sm,cv=kf, scoring='roc_auc')
print ('Bias score:',np.mean(score_AB))
print('Variance error:',np.std(score_AB,ddof=1))

Bias score: 0.9662572960598312
Variance error: 0.002853757720574624


#more than our own tuned Decision tree model. ADA boosted model gives better performance


#boosting base Logistic regression model
AB=AdaBoostClassifier(base_estimator=lrsm,random_state=10)
params={'n_estimators':np.arange(1,100)}
kf=KFold(n_splits=5,shuffle=True,random_state=10)
gs=GridSearchCV(AB,params,cv=kf,scoring='roc_auc')
gs.fit(xtrain_sm,ytrain_sm)
gs.best_params_

{'n_estimators': 38}


#ada boosted logistic regression
ABLR=AdaBoostClassifier(base_estimator=lrsm,n_estimators=38,random_state=10)
ABLR.fit(xtrain_sm, ytrain_sm)
pred_ABLR = ABLR.predict(xtest_scaled)
print (classification_report (ytest, pred_ABLR))

              precision    recall  f1-score   support

           0       0.95      0.90      0.92      3115
           1       0.58      0.76      0.66       584

    accuracy                           0.88      3699
   macro avg       0.77      0.83      0.79      3699
weighted avg       0.89      0.88      0.88      3699


kf=KFold (n_splits =5, shuffle = True, random_state = 10)
score_ABLR = cross_val_score (ABLR, xtrain_sm, ytrain_sm,cv=kf, scoring = 'roc_auc')
print ('Bias score:',np.mean(score_ABLR))
print ('Variance score:',np.std(score_ABLR,ddof=1))

Bias score: 0.9333297124266646
Variance score: 0.004002160496831838


#by boosting we have improved the bias score of LR and Decision Tree classifier. Though the Random forest performs well
#now we are going to stack the top two models


modelss=[]
modelss.append(('Logistic',lrsm))
modelss.append(('NaiveBayes',gnbsm))
modelss.append(('KNN',knnsm))
modelss.append(('DecisionTree',dtsm))
modelss.append(('RandomForest',rfsm))
modelss.append(('decision_tree_tuned',dtsm_Tuned))
modelss.append(('KNN_tuned',knnsm_Tuned))
modelss.append(('Randomforest_Tuned',rfsm_Tuned))
modelss.append(('AdaboostedDT',AB))
modelss.append(('AdaboostedlogisticRegression',ABLR))


dataa=[]
dataa1=[]
resultss=[]
namess=[]
for name , model in modelss:
    kfold =KFold (n_splits =5, shuffle = True, random_state = 10)
    cv_resultss=cross_val_score(model,xtrain_sm,ytrain_sm,scoring='roc_auc')
    resultss.append(cv_resultss)
    namess.append(name)
    dataa.append(np.mean(cv_resultss))
    dataa1.append(np.std(cv_resultss,ddof=1))
    az = pd.DataFrame({'Bias score':dataa,'Variance error':dataa1},index=namess)
az.head(10)


#among these Adaboosted DT and random forest are the best models..so we are going to stack together these 2 models


from sklearn.ensemble import VotingClassifier
Stacked = VotingClassifier(estimators = [('AdaboostedDT',AB),
 ('RandomForest',rfsm)],voting='soft')


modelss=[]
modelss.append(('Logistic',lrsm))
modelss.append(('NaiveBayes',gnbsm))
modelss.append(('KNN',knnsm))
modelss.append(('DecisionTree',dtsm))
modelss.append(('RandomForest',rfsm))
modelss.append(('decision_tree_tuned',dtsm_Tuned))
modelss.append(('KNN_tuned',knnsm_Tuned))
modelss.append(('Randomforest_Tuned',rfsm_Tuned))
modelss.append(('AdaboostedDT',AB))
modelss.append(('AdaboostedlogisticRegression',ABLR))
modelss.append(('Stackedmodels',Stacked))


dataa=[]
dataa1=[]
resultss=[]
namess=[]
for name , model in modelss:
    kfold =KFold (n_splits =5, shuffle = True, random_state = 10)
    cv_resultss=cross_val_score(model,xtrain_sm,ytrain_sm,scoring='roc_auc')
    resultss.append(cv_resultss)
    namess.append(name)
    dataa.append(np.mean(cv_resultss))
    dataa1.append(np.std(cv_resultss,ddof=1))
    az = pd.DataFrame({'Bias score':dataa,'Variance error':dataa1},index=namess)


az.head(11)


#among these stacked model performs well and it can be further moved to deployment.

	count	mean	std	min	25%	50%	75%	max
Administrative	12330.0	2.315166	3.321784	0.0	0.000000	1.000000	4.000000	27.000000
Administrative_Duration	12330.0	80.818611	176.779107	0.0	0.000000	7.500000	93.256250	3398.750000
Informational	12330.0	0.503569	1.270156	0.0	0.000000	0.000000	0.000000	24.000000
Informational_Duration	12330.0	34.472398	140.749294	0.0	0.000000	0.000000	0.000000	2549.375000
ProductRelated	12330.0	31.731468	44.475503	0.0	7.000000	18.000000	38.000000	705.000000
ProductRelated_Duration	12330.0	1194.746220	1913.669288	0.0	184.137500	598.936905	1464.157213	63973.522230
BounceRates	12330.0	0.022191	0.048488	0.0	0.000000	0.003112	0.016813	0.200000
ExitRates	12330.0	0.043073	0.048597	0.0	0.014286	0.025156	0.050000	0.200000
PageValues	12330.0	5.889258	18.568437	0.0	0.000000	0.000000	0.000000	361.763742
SpecialDay	12330.0	0.061427	0.198917	0.0	0.000000	0.000000	0.000000	1.000000
OperatingSystems	12330.0	2.124006	0.911325	1.0	2.000000	2.000000	3.000000	8.000000
Browser	12330.0	2.357097	1.717277	1.0	2.000000	2.000000	2.000000	13.000000
Region	12330.0	3.147364	2.401591	1.0	1.000000	3.000000	4.000000	9.000000
TrafficType	12330.0	4.069586	4.025169	1.0	2.000000	2.000000	4.000000	20.000000

	Bias score	Variance error
Logistic	0.911256	0.013996
NaiveBayes	0.567484	0.076740
KNN	0.783732	0.018745
DecisionTree	0.735969	0.029659
RandomForest	0.925249	0.007630

	Bias score	Variance error
Logistic	0.934042	0.007397
NaiveBayes	0.588860	0.061882
KNN	0.942815	0.013436
DecisionTree	0.882065	0.047925
RandomForest	0.982866	0.017025
decision_tree_tuned	0.950372	0.023677
KNN_tuned	0.957399	0.010439
Randomforest_Tuned	0.982510	0.017283
AdaboostedDT	0.964561	0.031178
AdaboostedlogisticRegression	0.933734	0.007401

	Bias score	Variance error
Logistic	0.934042	0.007397
NaiveBayes	0.588860	0.061882
KNN	0.942815	0.013436
DecisionTree	0.882065	0.047925
RandomForest	0.982568	0.017991
decision_tree_tuned	0.950428	0.024004
KNN_tuned	0.957399	0.010439
Randomforest_Tuned	0.982446	0.017489
AdaboostedDT	0.964561	0.031178
AdaboostedlogisticRegression	0.933734	0.007401

	Bias score	Variance error
Logistic	0.934042	0.007397
NaiveBayes	0.588860	0.061882
KNN	0.942815	0.013436
DecisionTree	0.882065	0.047925
RandomForest	0.982568	0.017991
decision_tree_tuned	0.950428	0.024004
KNN_tuned	0.957399	0.010439
Randomforest_Tuned	0.982446	0.017489
AdaboostedDT	0.964561	0.031178
AdaboostedlogisticRegression	0.933734	0.007401
Stackedmodels	0.982836	0.017431

Dataset Details¶

Importing necessary libraries¶

Importing the Dataset¶

Dataset information¶

Null values in the Dataset¶

Visualization¶

Univariate Analysis¶

Bi-Variate Analysis¶

Outlier Detection¶

there are outliers,we already seen it in the box plot also¶

Multicollinearity¶

Type Casting¶

Categorical Column Treatment¶

Multicollinearity¶

Checking for Class Imbalance¶

Performing relevant tests to find out the Independent Variables are significant to the Target variable¶

The Numerical columns is tested for two sample t test and Categorical columns is tested for chi square test.¶

Assumptions of two sample t test- Shapiro test and levene test.¶

Train Test Split and Scaling¶

Checking whther train and test representative of the overall data¶

Scaling¶

Base Model Building¶

Evaluating the base models¶

Building Base model with class imbalance treatment¶

Tuning the base model¶

Results after Tuning¶

Boosting¶

Stacking¶

	ProductRelated	ProductRelated_Duration	BounceRates	ExitRates	Month	OperatingSystems	Browser	Region	TrafficType	VisitorType	Weekend	Revenue
0	1	0.000000	0.20	0.20	Feb	1	1	1	1	Returning_Visitor	False	False
1	2	64.000000	0.00	0.10	Feb	2	2	1	2	Returning_Visitor	False	False
2	1	0.000000	0.20	0.20	Feb	4	1	9	3	Returning_Visitor	False	False
3	2	2.666667	0.05	0.14	Feb	3	2	2	4	Returning_Visitor	False	False
4	10	627.500000	0.02	0.05	Feb	3	3	1	4	Returning_Visitor	True	False

	pval
Administrative	6.55966e-78
Administrative_Duration	2.33613e-74
Informational	6.01391e-37
Informational_Duration	7.04519e-36
ProductRelated	5.26219e-108
ProductRelated_Duration	2.66613e-128
BounceRates	9.16385e-62
ExitRates	5.78412e-176
PageValues	0
SpecialDay	2.53978e-22
OperatingSystems	0.00120423
Browser	0.035731
Region	0.050246
TrafficType	0.447477
Weekend	0.000571296
Revenue	0

	Bias score	Variance error
Logistic1	0.933510	0.004280
NaiveBayes1	0.597338	0.058223
KNN1	0.942917	0.005659
DecisionTree1	0.883680	0.006156
RandomForest1	0.982146	0.001720